Income or Job Loss and Psychological Distress During the COVID-19 Pandemic

Key Points Question What is the association between income or job loss in the early phase of the COVID-19 pandemic and later psychological distress? Findings In this cohort study of 1392 US working-age adults, income or job loss between March 24 and August 16, 2020, was associated with psychological distress scores that were 1.09 and 1.11 times higher in February 2021 and September 2022, respectively, compared with individuals with no income or job loss. Meaning These findings suggest the need for policies to support people with income or job loss to help mitigate long-term adverse mental health outcomes of economic disruption.


eMethods
We used a sample of participants from the Pew Research Center American Trends Panel (ATP) with data from Wave 54 (September 16-29, 2019) for pre-pandemic financial standing and parental status, Wave 64 (March 19-24, 2020) for pre-income or job loss psychological distress and demographics, Wave 72 (August 3-16, 2020) for income or job loss, Wave 83 (February 16-21, 2021) and Wave 114 (September 13-18, 2022) for post income or job loss psychological distress.Questionnaires for each wave, containing the exact wording of the questions, can be downloaded at https://www.pewresearch.org/american-trends-panel-datasets/.

Survey Methodology
The ATP consists of a nationally representative sample of U.S. non-institutionalized adults who completed periodic surveys ("Waves").When the ATP began in 2014, recruitment was conducted via random telephone number dialing, but as of 2018, participants are recruited via mail with random stratified sampling based on address. 1 Each wave contains questions pertaining to a variety of topics and particular questions may be repeated across multiple waves and or only asked in a single wave.Demographic information is collected periodically, but not in every wave 2 .All questions are multiple choice. 1 Panelists receive notification of the survey via email, SMS, or mail (by panelist preference) and complete the survey over the internet (with tablet and internet connection access available on request for those without internet access), in either English or Spanish. 1 Panelists receive differential incentives to complete surveys based on the relative difficulty of obtaining data from different subpopulations. 1 Out of 7,347 invited panelists, 6878 (93.6%) completed Wave 54 survey in September 16-29, 2019. 3During Wave 64 (March 19-24, 2020) 11,537 out of 15,433 invited panelists (74.8%) completed the survey. 4Between August 3 and 16, 2020, 13,200 out of 15,387 invited panelists (85.8%) completed the Wave 72 survey. 5Between February 16 and 21, 2021, 10,121 out of 11,605 invited panelists (87.2%) completed the Wave 83 survey. 6During Wave 114 (September 13-18, 2022), 10,588 out of 11,687 invited panelists (90.6%) completed the survey. 1 There were N=3335 individuals who completed all five of these waves.

Survey Weighting
The Pew Research Center created customized survey weights calibrated for our sample, based on the weights for our baseline wave (Wave 54), adjusted for selection and non-response at the subsequent waves, and trimmed at the 1st and 99th percentiles. 3Survey weights align the sample with the U.S. population of adults ages 18-64 years who did not experience household income or job loss prior to March 24, 2020.The estimates of the source population for Wave 54 primary came from the 2017 American Community Survey. 1

Missing data
Where possible, missing demographic data and income tiers were imputed using the closest non-missing time point out of the five waves analyzed.There was no missingness for census region or metropolitan area variables.After imputation using available data from the closest wave in time, there was no missingness in age, sex, or education.Some missingness remained in race and ethnicity, marital status, citizenship, income tier.Lifetime diagnosis of mental illness, parental status, financial strain, pre-pandemic employment, savings account, and debt were not imputed given that they were only collected at one timepoint.

Sample definition
Of the N=3335 who completed all five waves of interest, we excluded n=794 (23.8%) who were aged 65 or older.Of the remaining 2541 participants, the n=796 (31.3%) who reported "Yes, has happened" to having experienced self or household income or job loss due to the pandemic by March 2020 were excluded.Next, of the remaining 1745 participants, we excluded n=272 (15.6%) who reported self or household income or job loss as "Not applicable" in August 2020, leaving 1473 participants.Finally, we excluded participants missing data for self or household income loss (n=1 ,<1%), for any of the mental health measures (anxiety, depression, loneliness, lack of hope, sleep problems) used to construct psychological distress (n=37, 2.5%), or for any of the covariates (n=43, 2.9%).This defined our analytic sample consisting of N=1392 working aged adults in the U.S.

Measure descriptions
Income or job loss.In Wave 64 (March 2020), the survey questionnaire asked all participants: "For each of the following, indicate whether or not it is something that happened to YOU OR SOMEONE IN YOUR HOUSEHOLD because of the coronavirus outbreak: a. Been laid off or lost a job, b.Had to take a cut in pay due to reduced hours or demand for your work."The response options were "Yes, has happened" and "No, has not happened."In August 2020, the questionnaire first asked all participants: "For each of the following, indicate whether or not it is something that happened to YOU because of the coronavirus outbreak: a. Been laid off or lost a job, b.Had to take a cut in pay due to reduced hours or demand for your work."The response options were "Yes, has happened," "No, has not happened," and "Not applicable, not employed".Then, participants who had indicated that their household size included more than one adult besides themselves were asked "What about other people currently living in your household?Because of the coronavirus outbreak has ANYONE ELSE in your immediate household..." with the same options and responses available.Therefore, in our combined measure of self or household income or job loss, we coded participants as "Not applicable" if they either reported "Not applicable (not employed)" for both measures of self-income or job loss and their values for household income or job loss were both missing, or if they reported "Not applicable (not employed)" for all four measures.If a participant reported "Not applicable (not employed)" for one measure of self-income or job loss but "Yes, has happened" or "No, has not happened" for the other measure of self-income or job loss, we considered the other response to override the "Not applicable (not employed)" response.The same was done for household income or job loss, and the two measured were combined to create our composite measure which we refer to as income or job loss for parsimony.

Psychological Distress
Participants were asked "In the past 7 days, how often have you... a. Felt nervous, anxious, or on edge? b.Felt depressed?c.Felt lonely?d.Felt hopeful about the future?e. Had trouble sleeping?"The response choices were "Rarely or none of the time (less than 1 day)", "Some or a little of the time (1-2 days)", "Occasionally or a moderate amount of time (3-4 days)", and "Most or all of the time (5-7 days)", coded as 0, 1, 2, and 3 respectively (with hopefulness reverse coded).The composite psychological distress score was created by summing the scores on the five domains.The maximum possible score would be 15, corresponding to reporting "Most or all of the time (5-7 days)" for each of the five domains.The minimum possible score would be 0, corresponding to reporting "Rarely or none of the time (less than 1 day)" for each of the five domains.
Lifetime diagnosis of a mental health condition.In March 2020, participants were asked whether a doctor or other health care provider had ever told them they had a mental health condition.
Pre-pandemic finances and employment: We used the following variables, reported in September 2019: an indicator of secure debt (mortgage/home loans or car loans), an indicator of unsecured debt (credit card, student loans or medical debt), an indicator of financial strain (worrying about bills every day or almost every day), income tier (reported by the Pew Research Center, based on family income adjusted for household size and cost-of-living), and employment status (full-time, part-time, or not employed).Information on retirement status was not available.
Demographics: We used sex, age, race and ethnicity, marital status, education level, citizenship, census region, a metropolitan area indicator, and parental status as demographic covariates.Sex was reported as a binary (male or female) variable.Race and ethnicity were reported as a combined, mutually exclusive construct in four categories: "White non-Hispanic", "Black non-Hispanic", "Hispanic" and "Other".Due to the small number of participants reporting their marital status as "Separated," we created a composite category representing both separated and divorced participants.March 2020 (Wave 64) data was used for all demographics, except parental status (whether the participant was a parent to a child under the age of 18) which was only reported in September 2019.

Statistical model choice
We modelled the relationship between income or job loss and psychological distress using quasi-Poisson models.][9] The quasi-Poisson model assumes a functional form for the conditional mean with a log-link and that the conditional variance is proportional to the conditional mean, allowing for over-or under-dispersion (meaning that the variance is not equal to the mean, as a traditional Poisson model assumes). 7Although quasi-Poisson models are most commonly used to model counts of events, they can also be used with non-negative integer data or even continuous positive data.
While ordinary least squares (OLS) models are the most common choice for modelling psychometric data, quasi-Poisson models have also been used in the literature [11][12][13][14][15] .For data that is truly distributed as a Poisson with a relatively low mean, OLS may result in biased standard errors. 9,16Another possible model for this data would be a OLS model with a log transformation of the outcome, however, these models have been show to perform poorly under heteroskedasticity 10 and when some proportion of our data is zero-valued. 17though we use the quasi-Poisson model in our main analysis, given the prevalence of OLS models in the literature, we also conduct a supplemental analysis replacing the quasi-Poisson model with an OLS model (see eResults below).

eResults Propensity score and weight distribution
As described in the main text, we computed propensity scores and corresponding weights using logistic regression, with demographics, pre-pandemic financial status, life-time diagnosis of a mental illness, and baseline psychological distress included in the model to estimate the probability of experiencing income or job loss between Match 24, 2020 August 16, 2020.
We graphically examined the distribution of propensity scores in the two groups to check for common support (eFigure 2) and saw overlap between the groups across approximately the entire range of propensity scores in the sample.Propensity scores ranged from 0.11-0.71(median=0.34)and 0.11-0.79(median=0.31) in the groups who did and did not report income or job loss, respectively.
To weight our sample to the group who experienced income or job loss, those in this group are assigned a propensity score weight of 1, and those who did not experience income or job loss were weighted by their odds of experiencing income or job loss.These weights ranged from 0.13-3.85(median=0.46) in the group who did not report income or job loss, as seen in eFigure 3. Based on this qualitative evaluation we did not consider any of these weights to be extreme or require trimming.

Analysis using an OLS model
To investigate the robustness of our findings, we repeated the main analysis using an ordinary least squares model instead of a quasi-Poisson.We estimate that on a difference in means scale, early phase pandemic income or job loss was associated increases of 0.42 (95% CI=0.00-0.84,p=0.05) and 0.52 (95% CI=0.05-0.98,p=0.03) points higher, respectively.These estimates and CIs are of similar magnitude to the quasi-Poisson estimates and support the conclusions discussed in the main text.

eFigure 1. Flow Chart of Sample Inclusion and Survey Measures
eFigure 1: Using data from Waves 54, 64, 72, 83, and 114 of the of Pew Research Center's American Trends Panel data, we subset our sample to only those who responded to all five waves, were younger than age 65, did not report self or household income or job loss due to the pandemic by March 2020, did not report reported self or household income or job loss as "Not applicable" in August 2020, and were not missing data on the variables of interest.

eFigure 2 .eFigure 2 :eFigure 3 .eFigure 3 :
Distribution of Propensity Scores by Income or Job Loss Group The distribution of propensity scores (estimated probability of experiencing income or job loss between March 24, 2020 and August 16, 2020) is shown among the groups who did and did not experience income or job loss, in our sample of adults 18-64 years in the U.S. who had not experienced income or job loss by March 24, 2020 (N=1392).Data comes from the of Pew Research Center's American Trends Panel data.Job or income lossNo Yes density Distribution of Propensity Score Weights Among Those Who Did Not Experience Income A histogram of propensity score weights is shown for those who did not report income or job loss byAugust 16, 2020  (n=955).Those who did report income or job loss between March 24, 2020 and August 16, 2020 are assigned weights of one.Data comes from the of Pew Research Center's American Trends Panel data.Full analytic sample and missing data sample 81(6.4)1392 (93.

a
This ratio is obtained by exponentiating the coefficient of the survey and propensity score weighted multivariable quasi-Poisson model and is presented with 95% confidence intervals and p-value.b This difference is obtained from the quasi-Poisson models using the marginal effect package in R to compute the point estimate, 95% confidence intervals and p-value.c,d Demographics, pre-pandemic finances, life-time diagnosis of a mental health condition are included as covariates in all three models, and psychological distress in March 2020 is included as a covariate for the late-pandemic models.All these covariates are included in the propensity score model.Data: Pew Research Center's American Trends Panel.Sample: Adults 18-64 years in the U.S. who had not experienced income or job loss by March 24, 2020 (N=1392).

Propensity Score Model a and Covariate Balance Propensity score model a Covariate balance (SMD) b
The missing data sample consists of participants who are missing data for at least one covariate, psychological distress measurement, or early-pandemic income or job loss.The sample of participants not missing data is the analytic sample used in the main text, which consists of U.S. adults aged 18-64 who did not experience income or job loss before March 24, 2020 from the Pew Research Center's American Trends Panel.b Number of participants (No.) is unweighted, percentage (%) is weighted with longitudinal survey weights.Missing data is excluded for in percentage calculations to facilitate comparison between groups.c Mean and standard deviation are weighted with longitudinal survey weights.Missing data is excluded for in percentage calculations to facilitate comparison between groups.d Psychological distress measured on a composite scale from 0-15 based on participants' reported frequency of feeling depressed, on edge, sleepless, lonely, and hopeless in the last week).Psychological distress in March 2020 is reported prior to any of this sample experiencing household income or job loss and is used as a covariate in our main analysis.February 2021 and September 2022 psychological distress are the dependent variables of interest in our main analysis.Estimated Association Between Early-Pandemic Income or Job Loss and Pandemic Psychological Stress Score a a We used a multivariable survey weighted logistic regression to model the probability of experiencing income or job loss between March 24, 2020 -August 16, 2020.Adjusted odds ratio (aOR), 95% confidence intervals (CI), and p-value are reported for each covariate.The analytic sample consists of U.S. adults aged 18-64 who did not experience income or joss before March 24, 2020.b Standardized mean difference (SMD).Unadjusted: Survey weighted only.Adjusted: Survey and propensity score weighted.c A dash indicates a reference level.For two-category variables, reference level is not displayed.eTable 3.